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. 2023 Jul 3;2(1):14.
doi: 10.1186/s44167-023-00023-7.

Characteristics associated with differences in 24-hour device-measured and self-reported sleep, sedentary behaviour and physical activity in a sample of Australian primary school children

Joshua Gauci  1 , Timothy Olds  1   2 , Carol Maher  1 , Amanda Watson  1 , François Fraysse  1 , Mason Munzberg  1 , Isaac Hoepfl  1 , Dorothea Dumuid  3   4
Affiliations

Characteristics associated with differences in 24-hour device-measured and self-reported sleep, sedentary behaviour and physical activity in a sample of Australian primary school children

Joshua Gauci et al. J Act Sedentary Sleep Behav. .

Abstract

Background: How much time children spend sleeping, being sedentary and participating in physical activity affects their health and well-being. To provide accurate guidelines for children's time use, it is important to understand the differences between device-measured and self-reported use-of-time measures, and what may influence these differences. Among Australian primary school-aged children, this study aimed to describe the differences between device-measured and self-reported sleep, sedentary behaviour, light-intensity physical activity (LPA), and moderate-vigorous-intensity physical activity (MVPA), and to explore how sociodemographic and personal characteristics were associated with these differences.

Methods: Participants (n = 120, 67% female, age 9-11 years) were drawn from the Life on Holidays cohort study. Device measured use of time was from 7-day accelerometry worn over five timepoints in a 2-year period, and self-reported use of time was from 2-day Multimedia Activity Recall for Children and Adults (MARCA), conducted at the same timepoints. For each participant and measurement method, average daily time spent in sleep, sedentary time, LPA and MVPA was derived for any overlapping days (that had both types of measurement) across the study period. Participant characteristics were either obtained from baseline parental survey (age, sex, parental education, puberty) or derived from the average of direct measurements across the study timepoints (aerobic fitness from shuttle run, body mass index from anthropometric measurements, academic performance from national standardised tests). Differences between device-measured and self-reported use of time were described using Bland-Altmann plots. Compositional outcome linear-regression models were used to determine which participant characteristics were associated with differences by use-of-time measurement type.

Results: Relative to device-measured, self-reported daily LPA was underestimated by 83 min (35% difference), whilst sleep (+ 37 min; 6% difference), MVPA (+ 34 min; 33% difference) and sedentary time (+ 12 min; 3% difference) were overestimated. Characteristics underpinning the differences between measurement types were sex (χ2 = 11.9, p = 0.008), parental education (χ2 = 23.0, p = 0.001), aerobic fitness (χ2 = 10.7, p = 0.01) and academic performance (χ2 = 15.9, p = 0.001).

Conclusions: Among primary school-aged children, device-measured and self-reported use-of-time measurements should not be used interchangeably as there are systematic biases and differences relative to socio-demographic characteristics.

Keywords: 24-hour recall; Accelerometry; Physical activity; Sedentary; Sleep; Time use.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: Ethical approval was obtained from the University of South Australia Human Research Ethics Committee, Adelaide, Australia (200980), the South Australian Department of Education and Child Development (2008-0055) and Catholic Education South Australia (201820). Written informed consent was obtained by a parent/guardian, and verbal assent to proceed with a test or measurement was obtained from participants. Any child, or parent/guardian on behalf of the child, could withdraw from the study at any time without penalty. Consent for publication: NA. Competing interests: The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Participant Flow Abbreviations: PAT: progressive achievement test, BMI: body mass index
Fig. 2
Fig. 2
Bland-Altman Plots for differences in measurement of each behaviour NB Average measurement (x-axis) refers to the average of device measured (accelerometry) and self-reported (MARCA) use of time Blue line: average/bias; red lines: 95% confidence interval Abbreviations: LPA: light physical activity; MVPA: moderate-to-vigorous physical activity; Self-R: self-reported (MARCA); Device-M: device-measured (accelerometer); MARCA: Multimedia Activity Recall for Children and Adults
Fig. 3
Fig. 3
Model estimated use-of-time compositions for device and self-reported measurement methods across child characteristics (n = 120) NB. All models are adjusted for repeated measurements within participants, schools, and data collection waves using random intercepts. Random slopes for ILR numbers are included due to the long model format. Significant interaction effects are indicated by an asterisk (*) in the column header. In the academic performance (PAT) model, the sample size is n = 103. Abbreviations: Self-R: self-reported (MARCA); Device-M: device-measured (accelerometer); MARCA: Multimedia Activity Recall for Children and Adults, zBMI: body mass index z-score; VO2max: maximal oxygen consumption; PAT: progressive achievement test; LPA: light physical activity; MVPA: moderate-to-vigorous physical activity
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